3d pose program

Depth Prediction3DV 2020Learning Monocular Dense Depth from EventsICCV 2019Learning an Event Sequence Embedding for Dense Event-Based Deep Stereo4 Domain Specific4.1 NeRF & 3D reconstructionPublicationTitleHighlight3DV 20243d pose estimation of two interacting hands from a monocular event cameraArxiv 2022EventNeRF: Neural Radiance Fields from a Single Colour Event CameraArxiv 2022Ev-NeRF: Event Based Neural Radiance FieldArxiv 2022E-NeRF: Neural Radiance Fields from a Moving Event CameraIJCV 2018EMVS: Event-Based Multi-View Stereo—3D Reconstruction with an Event Camera in Real-TimeArxiv 2020E3D: Event-Based 3D Shape ReconstructionECCV 2022EvAC3D: From Event-based Apparent Contours to 3D Models via Continuous Visual HullsArxiv 2022Event-based Non-Rigid Reconstruction from ContoursArxiv 2022Event-Based Dense Reconstruction PipelineECCV 2016Real-Time 3D Reconstruction and 6-DoF Tracking with an Event CameraICAR 2019Multi-View 3D Reconstruction with Self-Organizing Maps on Event-Based DataIEEE 2018Ultimate slam? combining events, images, and imu for robust visual slam in hdr and high-speed scenarios3DV 2021ESL: Event-based Structured LightECCV 2020Stereo Event-Based Particle Tracking Velocimetry for 3D Fluid Flow ReconstructionICCV 2019Learning an Event Sequence Embedding for Dense Event-Based Deep Stereo4.2 Human Pose and ShapePublicationTitleHighlight3DV 20243d pose estimation of two interacting hands from a monocular event cameraHand PoseCVPRW 2023MoveEnet: Online High-Frequency Human Pose Estimation With an Event CameraHuman PoseArxiv 2022Efficient Human Pose Estimation via 3D Event Point CloudArxiv 2022A Temporal Densely Connected Recurrent Network for Event-based Human Pose EstimationICCV 2021EventHands: real-time neural 3D hand pose estimation from an event streamHand PoseCVPR 2021Lifting Monocular Events to 3D Human PosesICCV 2021EventHPE: Event-based 3D Human Pose and Shape EstimationHuman PoseCVPR 2020EventCap: Monocular 3D Capture of High-Speed Human Motions Using an Event CameraHuman PoseWACV 2019Space-Time Event Clouds for Gesture Recognition: From RGB Cameras to Event CamerasHand PoseCVPR 2019DHP19: Dynamic Vision Sensor 3D Human Pose DatasetArxiv 2019EventGAN: Leveraging Large Scale Image Datasets for Event CamerasICCV 2021EventHPE: Event-Based 3D Human Pose and Shape Estimation4.3 Body and Eye TrackingPublicationTitleHighlightObject tracking on event cameras with offline–online learningReal-Time Face & Eye Tracking and Blink Detection using Event CamerasECCV 2020Stereo Event-based Particle Tracking Velocimetry for 3D Fluid Flow ReconstructionISFV 2014Large-scale Particle Tracking with Dynamic Vision SensorsT-CG 2021Event Based, Near-Eye Gaze Tracking Beyond 10,000HzDataset4.4 FacePublicationTitleHighlightSensor 2020Face pose alignment with event cameras4.5 CompressionPublicationTitleHighlightT-SPL 2020Lossless Compression of Event Camera Frames4.4 SAIPublicationTitleHighlightCVPR 2021Event-Based Synthetic Aperture Imaging With a Hybrid Network5 Robotic Vision5.1 Object Detection and TrackingThis section focuses on event-based detection/tracking tasks for Robotics implementation.PublicationTitleHighlightNeurIPS 2024EV-Eye: Rethinking High-frequency Eye Tracking through the Lenses of Event CamerasDLCVPR 2024Event Stream-based Visual Object Tracking: A High-Resolution Benchmark Dataset and A Novel BaselineDLCVPRW 2024A Lightweight Spatiotemporal Network for Online Eye Tracking with

Linux Video Effects SDKAugmented Reality SDK(Windows/Linux: 0.8.2)- Enable real-time 3D tracking of a person’s face using a standard web camera. Create unique AR effects such as overlaying 3D content on a face, driving 3D characters and virtual interactions in real time. Note: The Linux version of the Augmented Reality SDK is currently only available in the Early Access Program.Key FeaturesFace Tracking Face Landmark tracking Face Mesh Body Pose EstimationEye ContactFace Expression EstimationLatest Release Face Expression Estimation6DOF head pose now availableExpression estimation model updatedNew face model for visualization with updated blendshapes, and face area partitioningEye ContactPerformance improvements via CUDA graph functionalityOperating SystemsWindows 10, Windows 11 64-bit, Ubuntu 18.04, Ubuntu 20.04, CentOS 7Supported HardwareWindows SDK: NVIDIA GeForce RTX 20XX and 30XX Series, Quadro RTX 3000, TITAN RTX, or higher (any NVIDIA GPUs with Tensor Cores)Server SDK: V100, T4, A10, A30, A100 (with MIG support)Support for Ada-generation GPUs for Windows SDKsSoftware DependenciesWindows SDK: NVIDIA Display Driver 511.65+ or more recent, CMake 3.12+Server SDKs (Linux): CUDA 11.8.0, TRT 8.5.1.7, CuDNN 8.6.0.163, CMake 3.12+, NVIDIA Display Driver 520.61 or laterWindows AR SDK and Linux AR SDK (early access program)Getting started with MaxineProcedureFollow the resource cards for specifics on using each of the SDKs. SDK-specific programming guides are available inside Audio Effects SDK, Video Effects SDK, and Augmented Reality SDK Program Guides. You can also find them in the documentation which is available here.LicenseThe NVIDIA Maxine license agreement is contained in the SDK download packages. Please refer to the SDK packages for the SDK-specific licenses.Ethical AINVIDIA’s platforms and application frameworks enable developers to build a wide array of AI applications. Please consider potential algorithmic bias when choosing or creating the models being deployed. Work with the model’s developer to ensure:The model meets the requirements for the relevant industry and use caseThe necessary instruction and documentation are provided to understand error rates, confidence intervals, and resultsThe model is being used under the conditions and in the manner intended

Pose and shape via model-fitting in the loop. In ICCV, 2019. • I2L-MeshNet: Image-to-Lixel Prediction Network for Accurate 3D Human Pose and Mesh Estimation from a Single RGB Image. In ECCV, 2020. • Learning 3D Human Shape and Pose from Dense Body Parts. In TPAMI, 2020. • ExPose: Monocular Expressive Body Regression through Body-Driven Attention. In ECCV, 2020. • Hierarchical Kinematic Human Mesh Recovery. In ECCV, 2020. • Pose2Mesh: Graph Convolutional Network for 3D Human Pose and Mesh Recovery from a 2D Human Pose. In ECCV, 2020. • 主要思路：估计SMPL参数，加入2D keypoint loss，adversarial loss，silhouette loss等；有3D真值时可以加入SMPL参数真值、Mesh真值、3D joint真值约束；融合regression-based 和 optimization-based方法协作提升；从估计SMPL估计更精细的SMPL-X，对手部和头部强化处理； • 目前挑战：现实场景缺乏真值数据，如何产生有用的监督信号或pseudo ground-truth来帮助训练；合成数据有真值但存在domain gap，如何有效利用合成数据来帮助真实场景训练；目前很多方法估计结果在人体深度、肢体末端如手部和脚部还存在偏差，对复杂姿势估计结果仍不够准确； 动态视频 • Learning 3D Human Dynamics from Video. In CVPR, 2019. • Monocular Total Capture: Posing Face, Body, and Hands in the Wild. In CVPR, 2019. • Human Mesh Recovery from Monocular Images via a Skeleton-disentangled Representation. In ICCV, 2019. • VIBE: Video Inference for Human Body Pose and Shape Estimation. In CVPR, 2020. • PoseNet3D: Learning Temporally Consistent 3D Human Pose via Knowledge Distillation. In CVPR, 2020. • Appearance Consensus Driven Self-Supervised Human Mesh Recovery. In ECCV, 2020. • 主要思路：估计单帧SMPL参数基础上加入帧间连续性和稳定性约束；帧间联合优化；appearance一致性约束； • 目前挑战：帧间连续性和稳定性约束会对动作产生平滑效果，导致每一帧都不是很准确；估计出来的结果仍会存在漂浮、抖动、滑步等问题； 3D人体重建近年来与3D人体重建相关的工作很多，按照上述3D表示形式可分为基于Voxel表示、基于Mesh表示和基于Implicit function表示；按照输入形式可分为：基于单张图像、多视角图像和基于视频输入，这些输入都可以带有深度信息或无深度信息；按照重建效果可以分为带纹理重建和不带纹理重建，能直接驱动和不能直接驱动等等。 输入要求重建效果代表工作基本原理及评价 单张RGB图像 + 带衣服褶皱 + 带纹理 + 能直接驱动 • 360-Degree Textures of People in Clothing from a Single Image. In 3DV, 2019. • Tex2Shape: Detailed Full Human Body Geometry From a Single Image. In ICCV, 2019. • ARCH: Animatable Reconstruction of Clothed Humans. In CVPR, 2020. • 3D Human Avatar Digitization from a Single Image. In VRCAI, 2019. • 带衣服人体表示：SMPL+Deformation+Texture； • 思路1：估计3D pose采样部分纹理，再用GAN网络生成完整纹理和displacement； • 思路2：估计3D